Misbehavior aware on-demand intrusion detection system to enhance security in VANETs with efficient rogue nodes detection and prevention techniques

Vehicular ad-hoc networks (VANETs) facilitate vehicles to broadcast beacon messages to ensure road safety. The goal behind sharing the information through beacon messages is to disseminate network state or emergency information. The exchange of information is susceptible to security attacks of different kinds. Amongst various problems to be solved in VANETs is the issue of rogue nodes and their impact on the network. Rogue nodes are malicious vehicles that are vicious to cause severe damage to the network by modifying or altering false data in beacon messages that could lead to catastrophic consequences like trapping a group of vehicles, road accidents, vehicle collisions, etc. This thesis discusses the problems associated with the security VANETs in the presence of rogue nodes. We proposed three novel intrusion detection frameworks to detect the rogue nodes responsible for false information, Sybil, and platoon control maneuver attacks only by analyzing and comparing the beacon messages broadcast over the network. The novelty of our frameworks lies in containing network damage and securing VANETs from the harmful impact of rogue nodes. The proposed frameworks are simulated using SUMO, OMNET++, and VENTOS, and the results obtained have been presented, discussed, and compared to existing frameworks. Results show that the developed methods improve the systems’ performance compared to existing methods even when the number of rogue nodes increases in the region